{"id":"W4311529236","doi":"10.3390/rs14236154","title":"Urban Flood Detection Using TerraSAR-X and SAR Simulated Reflectivity Maps","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of New Brunswick","funders":"Natural Resources Canada; New Brunswick Innovation Foundation; Agriculture and Agri-Food Canada; Government of Canada; European Space Agency","keywords":"Remote sensing; Synthetic aperture radar; Flood myth; Land cover; Polarimetry; Environmental science; Urban area; Radar; Interferometric synthetic aperture radar; Interferometry; Computer science; Geology; Land use; Geography; Scattering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002740592,0.0001196458,0.0001121378,0.00005566659,0.0006045373,0.00003795897,0.00005372955,0.00002713794,0.0000755127],"category_scores_gemma":[0.00001042817,0.0001255489,0.00003367965,0.0002753357,0.00004427771,0.0001307197,0.0004043665,0.0001709266,0.00001011797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004083836,"about_ca_system_score_gemma":0.000004943205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002429895,"about_ca_topic_score_gemma":0.0002182268,"domain_scores_codex":[0.9989405,0.0001287963,0.0001238911,0.0003154651,0.0002609522,0.000230342],"domain_scores_gemma":[0.9996853,0.00001972319,0.00006642578,0.0001746257,0.00000317234,0.00005071078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005621399,0.00004926213,0.002163281,0.00001512811,0.0000431199,0.00007482087,0.0008365977,0.09068519,0.4275458,0.000003096788,0.0004642563,0.4780633],"study_design_scores_gemma":[0.0003095067,0.00007329113,0.002820289,0.000006471733,0.00003991942,0.00004103972,0.0002204516,0.9828484,0.003168708,0.0003017368,0.009979637,0.0001905694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866348,0.00002399397,0.01006285,0.00007456304,0.0002224136,0.0001986075,0.000001083228,0.00008009081,0.002701586],"genre_scores_gemma":[0.9912295,0.000005654413,0.008344575,0.0001225142,0.00003931741,8.573544e-9,0.00000323462,0.00001803708,0.0002371315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8921632,"threshold_uncertainty_score":0.5119736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548106667936017,"score_gpt":0.2470748895732809,"score_spread":0.2315938228939207,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}